PUBO$$_i$$: A Tunable Benchmark with Variable Importance

In this work, we present the benchmark generator PUBO $$_i$$ , Polynomial Unconstrained Binary Optimization, that combines subproblems to create instances of pseudo-boolean optimization problems. Any mono-objective pseudoboolean functions including existing classical problems can be expressed with Walsh functions. The tune main features such as problem dimension, non-linearity degree, and neutrality. Additionally, able properties similar those real-like combinatorial problems, goal is introduce notion variable importance. Indeed, importance decision variables tuned using three parameters. version presented here, consider four already used in Chook for benchmarking quantum computers algorithms a basis. We also impact parameters fitness landscape analysis empirically shows these significantly